
This book describes the use of smoothing techniques in statistics and includes both density estimation and nonparametric regression. Incorporating recent advances, it describes a variety of ways to apply these methods to practical problems. Although the emphasis is on using smoothing techniques to explore data graphically, the discussion also covers data analysis with nonparametric curves, as an extension of more standard parametric models. Intended as an introduction, with a focus on applications rather than on detailed theory, the book will be equally valuable for undergraduate and graduate students in statistics and for a wide range of scientists interested in statistical techniques.The text makes extensive reference to S-Plus, a powerful computing environment for exploring data, and provides many S-Plus functions and example scripts. This material, however, is independent of the main body of text and may be skipped by readers not interested in S-Plus.
This book investigates the practical application of smoothing techniques, specifically kernel methods, for density estimation and nonparametric regression in statistical data analysis. Authors Adelchi Azzalini and Adrian W. Bowman provide a framework that bridges the gap between theoretical statistical models and applied data exploration. By focusing on graphical representation and computational implementation, the text serves as a guide for researchers and students to extend standard parametric modeling through flexible, data-driven curves.
What You Will Find
Experts recognize this work as a foundational introduction to smoothing techniques that prioritizes application over abstract mathematical derivation. Readers frequently note that while the S-Plus code is dated, the underlying statistical methodology remains highly relevant for those seeking to understand nonparametric curve fitting.
Page Count:
208
Publication Date:
1997-11-13
Publisher:
Oxford University Press
ISBN-10:
0198523963
ISBN-13:
9780198523963
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